all tools / ml
As of the latest check, Glimind tracks 200 ml AI agent (MCP) tools, of which 0 are healthy right now. The table below ranks them live-first by real-time reliability.
| tool | status | what it does | |
|---|---|---|---|
| analyze io.github.AION-Analytics/aion-indian-market-intelligence | unknown | Analyze an Indian market headline and return a sector impact vector with stakeholder views. | alternatives |
| detect_bad_channels io.github.AImplifier/neuro-mcp | unknown | Automatically detect bad channels in the loaded data. | alternatives |
| run_ica io.github.AImplifier/neuro-mcp | unknown | Run Independent Component Analysis on the loaded data. | alternatives |
| apply_ica io.github.AImplifier/neuro-mcp | unknown | Apply ICA solution to remove artifacts. | alternatives |
| teta_resolve_intent teta-pi/teta-pi | unknown | flagship — TWIRA-ranked routing from a natural-language intent, with entity_types + min_trust filters | alternatives |
| query_calls argosvix/mcp-server | unknown | Recent LLM call records, filterable by provider / model / time range | alternatives |
| aggregate_calls argosvix/mcp-server | unknown | Aggregate LLM call data | alternatives |
| get_cost_summary argosvix/mcp-server | unknown | Aggregate cost / calls / tokens by provider or model | alternatives |
| get_percentiles argosvix/mcp-server | unknown | Get percentile metrics for LLM calls | alternatives |
| propose_eval_criteria argosvix/mcp-server | unknown | Propose evaluation criteria for LLM calls | alternatives |
| detect_anomaly argosvix/mcp-server | unknown | Detect anomalies in LLM call data | alternatives |
| classify_calls_batch argosvix/mcp-server | unknown | Classify a batch of LLM calls | alternatives |
| compute_power_spectrum KonstantinGerbig/spectra-mcp-server | unknown | run CLASS, write pk_<model>.csv | alternatives |
| plot_power_spectra KonstantinGerbig/spectra-mcp-server | unknown | two-panel figure: P(k) + data, ratio panel | alternatives |
| ingest_document shamprakash2000/gemini-knowledge-mcp-server | unknown | Chunks text, embeds via Gemini API, stores vectors in Pinecone, records metadata in PostgreSQL | alternatives |
| atlas_ml_predict io.github.IamNishant51/atlas-pipeline | unknown | ML Bug & Performance Prediction: Predict bugs before production with 70-85% accuracy. | alternatives |
| foldkit_probe_from_kappa sjgant80-hub/acg-mapper-mcp | unknown | κ or text → band + routing op + probe question | alternatives |
| foldkit_classify_kappa_band sjgant80-hub/acg-mapper-mcp | unknown | free-text utterance → κ band via marker phrases | alternatives |
| agent_provide_data formio/Universal Agent Gateway (UAG) | unknown | Instructs a generically trained agent to analyze existing submission data and provide its own data/analysis within a workflow scenario. | alternatives |
| battle_simulation MohdFaizanf1/AI-Pokemon-Arena--MCP-Server | unknown | Simulate battles between two Pokemon | alternatives |
| analyze_pokemon MohdFaizanf1/AI-Pokemon-Arena--MCP-Server | unknown | Analyze a Pokemon's strengths and weaknesses | alternatives |
| foldkit_classify_kappa_band sjgant80-hub/niceassos-mesh-mcp | unknown | free-text utterance → κ band via marker phrases | alternatives |
| foldkit_probe_from_kappa sjgant80-hub/niceassos-mesh-mcp | unknown | κ or text → band + routing op + probe question | alternatives |
| analyze_job_fit com.rolnia/mcp | unknown | Analyze how well your profile matches a job | alternatives |
| foldkit_depth_band sjgant80-hub/niceassos-spec-mcp | unknown | κ → named band (ground / perception / gate / heart / naming / recognition / collapse) | alternatives |
| foldkit_classify_kappa_band sjgant80-hub/niceassos-spec-mcp | unknown | free-text utterance → κ band via marker phrases | alternatives |
| foldkit_signal_survival sjgant80-hub/niceassos-spec-mcp | unknown | κ^depth signal survival + % | alternatives |
| foldkit_probe_from_kappa sjgant80-hub/niceassos-spec-mcp | unknown | κ or text → band + routing op + probe question | alternatives |
| capture_feedback io.github.IgorGanapolsky/thumbgate | unknown | Capture user feedback (thumbs-up/down) on a tool call decision to improve future gating | alternatives |
| foldkit_depth_band sjgant80-hub/digitaltwintopd-mcp | unknown | κ → named band (ground / perception / gate / heart / naming / recognition / collapse) | alternatives |
| foldkit_classify_kappa_band sjgant80-hub/digitaltwintopd-mcp | unknown | free-text utterance → κ band via marker phrases | alternatives |
| foldkit_signal_survival sjgant80-hub/digitaltwintopd-mcp | unknown | κ^depth signal survival + % | alternatives |
| foldkit_probe_from_kappa sjgant80-hub/digitaltwintopd-mcp | unknown | κ or text → band + routing op + probe question | alternatives |
| run_agent @plori/mcp | unknown | Invoke an agent and read its reply (blocking or fire-and-forget) | alternatives |
| attach_processor io.github.AImplifier/eeg-mcp | unknown | Attach a custom real-time processor to a session. | alternatives |
| trigger_patrol com.ruddia/being-mcp-server | unknown | Run patrol — extract scenes and generate memory nodes | alternatives |
| verboo_agent verboo-bridge | unknown | Executes a Verboo AI model as a sub-agent synchronously for short tasks, with read-only or write mode, and optional model selection. | alternatives |
| verboo_agent_start verboo-bridge | unknown | Starts an asynchronous Verboo sub-agent job, returning a job_id for later status/result retrieval. | alternatives |
| verboo_job verboo-bridge | unknown | Queries the status or result of an asynchronous Verboo agent job. | alternatives |
| compete respcode-mcp | unknown | Compare 4 AI models side-by-side | alternatives |
| foldkit_classify_kappa_band sjgant80-hub/fallstack-mcp | unknown | free-text utterance → κ band via marker phrases | alternatives |
| foldkit_probe_from_kappa sjgant80-hub/fallstack-mcp | unknown | κ or text → band + routing op + probe question | alternatives |
| foldkit_classify_kappa_band sjgant80-hub/fallscribe-mcp | unknown | free-text utterance → κ band via marker phrases | alternatives |
| estimate_roi mingxin-tech/Mingxin MCP Server | unknown | KV-cache tiering ROI estimate for a GPU cluster (faithful port of the reproducible Python model; mid-scenario estimates, not commitments) | alternatives |
| miko.persona @projectmiko/mcp-server | unknown | Get persona information via MIKO REST API | alternatives |
| miko.narrative @projectmiko/mcp-server | unknown | Get narrative information for a token address | alternatives |
| miko.insights @projectmiko/mcp-server | unknown | Get insights via MIKO REST API | alternatives |
| detect_language io.github.IntelagentStudios/mcp-file-processor | unknown | Detect whether content is code, natural language, or mixed. | alternatives |
| submit_architecture_design_job olk/architecture-pattern-mcp | unknown | Submits an architecture design job asynchronously and returns a job_id immediately for short-timeout clients. | alternatives |
| get_architecture_design_status olk/architecture-pattern-mcp | unknown | Polls the status and result of a previously submitted architecture design job using its job_id. | alternatives |
| log_analyze io.github.Invarato/jarroba-tools-logs | unknown | Format, number of events, levels, time range, errors with their root cause resolved, most frequent patterns. | alternatives |
| log_trends io.github.Invarato/jarroba-tools-logs | unknown | Compares each pattern against itself — returns what spiked, what is new, and what stopped appearing. | alternatives |
| log_around io.github.Invarato/jarroba-tools-logs | unknown | What else clustered around a moment — what started before is a candidate explanation, what came after is usually a consequence. | alternatives |
| log_patterns io.github.Invarato/jarroba-tools-logs | unknown | Groups lines into templates using Drain algorithm, returns rare lines. | alternatives |
| log_reduce io.github.Invarato/jarroba-tools-logs | unknown | Cuts to a character budget keeping errors with full trace, window before, and rare items; repeats collapse to [xN]. | alternatives |
| log_stacktrace io.github.Invarato/jarroba-tools-logs | unknown | Parses stack traces from Java/JVM, Python, Node, Go, .NET, Rust, Ruby, PHP — returns Caused by chain, root cause, and first frame of your co | alternatives |
| lyra.arrangement.coach MasterBot99/ableton-mcp-extended | unknown | Arrangement-Dichte analysieren, Vorschläge machen | alternatives |
| list_video_models PianoNic/ClaudioKitchen | unknown | List available video generation models with pricing. | alternatives |
| rerank PianoNic/ClaudioKitchen | unknown | Rerank documents by relevance to a query. | alternatives |
| describe_image PianoNic/ClaudioKitchen | unknown | Analyze an image (OCR/vision) and return a description. | alternatives |
| generate_image PianoNic/ClaudioKitchen | unknown | Create an image from a text prompt. | alternatives |
| generate_video PianoNic/ClaudioKitchen | unknown | Generate a video (supports image-to-video) asynchronously. | alternatives |
| create_embeddings PianoNic/ClaudioKitchen | unknown | Generate embedding vectors for text. | alternatives |
| list_models PianoNic/ClaudioKitchen | unknown | List available OpenRouter models with pricing. | alternatives |
| find_specialist_for_task barneywohl/Bay Run | unknown | Discover, evaluate, and serve a specialist model for a task on your labeled data. | alternatives |
| request_specialist barneywohl/Bay Run | unknown | Serve-or-capture: returns a serve pointer if a specialist exists, else records your demand. | alternatives |
| eval_models barneywohl/Bay Run | unknown | Evaluate candidate models on your labeled data. | alternatives |
| embed barneywohl/Bay Run | unknown | Generate embeddings for input text. | alternatives |
| rerank barneywohl/Bay Run | unknown | Rerank documents by relevance to a query. | alternatives |
| classify barneywohl/Bay Run | unknown | Classify text for guardrail/moderation/sentiment/intent, or zero-shot via candidate_labels and an NLI model. | alternatives |
| foldkit_probe_from_kappa sjgant80-hub/falllegalpaper-mcp | unknown | κ or text → band + routing op + probe question | alternatives |
| foldkit_classify_kappa_band sjgant80-hub/falllegalpaper-mcp | unknown | free-text utterance → κ band via marker phrases | alternatives |
| larch_cauchy_wavelet xraylarch-mcp | unknown | Cauchy wavelet transform for simultaneous k- and R-resolution | alternatives |
| larch_lcf xraylarch-mcp | unknown | Linear combination fitting against standards | alternatives |
| larch_pca xraylarch-mcp | unknown | Principal Component Analysis | alternatives |
| larch_peak_fit xraylarch-mcp | unknown | Peak fitting (Gaussian, Voigt, etc.) | alternatives |
| sleep__predict @kamilio/baby-daybook-sdk | unknown | Predict sleep recommendations | alternatives |
| gate @cohesionauth/sdk | unknown | Gates a decision using DRS before or after AI output, returning routing decision. | alternatives |
| score @cohesionauth/sdk | unknown | Scores a human-AI interaction and returns the JIS (Judgment Independence Score) envelope. | alternatives |
| decisionScore @cohesionauth/sdk | unknown | Scores a decision for risk (DRS) and returns routing information. | alternatives |
| docqa-verify com.agentsconsultants.api/docqa | unknown | Verify AI-extracted fields against the original document, detecting hallucinations, arithmetic errors, and field mismatches. | alternatives |
| ocr com.agentsconsultants.api/docqa | unknown | Extract text from images using Tesseract OCR. | alternatives |
| avocado-interpret-image @avocadostudio-ai/mcp-server | unknown | Interprets an image | alternatives |
| conversation_ai NerdSnipe-Inc/GoHighLevel MCP | unknown | CRUD conversation AI agents, attach/manage agent actions, follow-up settings, generation | alternatives |
| voice_ai NerdSnipe-Inc/GoHighLevel MCP | unknown | CRUD voice AI agents & actions, call logs | alternatives |
| evaluate_llm Prasanjeet1982/Enterprise AI Toolkit MCP | unknown | Evaluates LLM responses across correctness, relevance, faithfulness | alternatives |
| recommend_llm Prasanjeet1982/Enterprise AI Toolkit MCP | unknown | Recommends optimal LLM based on budget, latency, and privacy | alternatives |
| track_record ArtBreguez/greeks-mcp | unknown | Aggregated signal accuracy (~last 35 days) | alternatives |
| cuba_hebbian io.github.LeandroPG19/memory-industry | unknown | Trigger Hebbian learning to strengthen connections between co-occurring facts | alternatives |
| planScan cx-anand-nandeshwar/DLP MCP Server | unknown | Recommend scan engines based on the project | alternatives |
| list_models ReverserID/rembg-mcp | unknown | Curated model catalog. | alternatives |
| gpu_status ReverserID/rembg-mcp | unknown | Show ONNX Runtime providers and whether CUDA is active. | alternatives |
| unload_sessions ReverserID/rembg-mcp | unknown | Free loaded models from GPU/CPU memory. | alternatives |
| vault_go_embedding_status vault-go | unknown | Busca híbrida e fila vetorial, sem expor chaves. | alternatives |
| vault_go_embedding_backfill vault-go | unknown | Busca híbrida e fila vetorial, sem expor chaves. | alternatives |
| get_predictive_maintenance_report jdbruh18/IndustrialOps Industry 4.0 PLC Monitor MCP Server | unknown | Analyzes machinery vibration telemetry and temperatures to estimate Remaining Useful Life (RUL) hours and schedule preventative inspections. | alternatives |
| audience_signals @trillboards/ads-sdk | unknown | Get real-time audience signals including face detection, gaze tracking, and attention measurement | alternatives |
| kimi_read_image kimi-read-image-mcp | unknown | Analyze a local image file by sending it as an inline base64 image_url part to a Kimi-compatible endpoint. | alternatives |
| prepare_visual_baseline tianshu-mcp | unknown | 准备视觉验收基线 | alternatives |
| approve_visual_baseline tianshu-mcp | unknown | 批准视觉验收基线 | alternatives |
| evaluate_model troxy-cli | unknown | Check before running a task on a model, and on any model or effort switch | alternatives |
| report_model_usage troxy-cli | unknown | Report the real token total after model usage | alternatives |
| ai_readiness_check DedeGroup/listinggood-skills | unknown | Score how likely Amazon's AI (Rufus, COSMO) is to recommend a listing; returns a combined AI Recommendation Readiness Score with actionable | alternatives |
| analyze_review DedeGroup/listinggood-skills | unknown | Analyze a negative review for root cause and suggest a response or POA angle. | alternatives |
| analyze_claims Fabric Pattern Tools | unknown | Analyze claims in text | alternatives |
| summarize Fabric Pattern Tools | unknown | Summarize text | alternatives |
| extract_wisdom Fabric Pattern Tools | unknown | Extract wisdom from text | alternatives |
| process_query Adaptive Graph of Thoughts | unknown | Process a scientific query through the ASR-GoT reasoning pipeline | alternatives |
| openrouter_list_models Erfouni/OpenRouter Model Router MCP Server | unknown | Search the current OpenRouter catalog | alternatives |
| openrouter_run_model Erfouni/OpenRouter Model Router MCP Server | unknown | Delegate one prompt to a requested model | alternatives |
| openrouter_compare_models Erfouni/OpenRouter Model Router MCP Server | unknown | Run the same prompt with 2–4 models | alternatives |
| summarize_daily_energy_context fralopmor-arch/mcp-SADE-ME | unknown | Utiliza un LLM (OpenAI) para interpretar el contexto del día y generar un reporte ejecutivo del comportamiento energético. | alternatives |
| foldkit_fold_number sjgant80-hub/foldkit MCP Server | unknown | 7-vector → primorial fold + signature | alternatives |
| foldkit_unfold_state sjgant80-hub/foldkit MCP Server | unknown | fold integer → 7-vector via 7-prime factorisation | alternatives |
| foldkit_depth_band sjgant80-hub/foldkit MCP Server | unknown | κ → named band (ground / perception / gate / heart / naming / recognition / collapse) | alternatives |
| foldkit_classify_kappa_band sjgant80-hub/foldkit MCP Server | unknown | free-text utterance → κ band via marker phrases | alternatives |
| auto_integration_time io.github.K-Dense-AI/labmcp-ocean-spectrometer | unknown | Adjust the integration time until the brightest raw pixel (optionally within a wavelength window) is within the target band of saturation (d | alternatives |
| create_agent bolna-ai/Bolna MCP Server | unknown | Create a new agent | alternatives |
| delete_agent bolna-ai/Bolna MCP Server | unknown | Permanently delete an agent | alternatives |
| get_voice bolna-ai/Bolna MCP Server | unknown | Get details of a specific voice by ID. | alternatives |
| update_agent bolna-ai/Bolna MCP Server | unknown | Patch an existing agent's settings | alternatives |
| list_batches bolna-ai/Bolna MCP Server | unknown | Call batches for one agent | alternatives |
| list_voices bolna-ai/Bolna MCP Server | unknown | Voices available for a TTS provider/model | alternatives |
| get_dataset akkireddy-challa/phoenix-mcp-eval | unknown | Fetch dataset examples for review or comparison. | alternatives |
| get_traces akkireddy-challa/phoenix-mcp-eval | unknown | Retrieve LLM traces for a project with optional filters. | alternatives |
| list_datasets akkireddy-challa/phoenix-mcp-eval | unknown | List evaluation datasets in Phoenix. | alternatives |
| list_evaluations akkireddy-challa/phoenix-mcp-eval | unknown | List evaluation runs and their scores. | alternatives |
| get_evaluation_summary akkireddy-challa/phoenix-mcp-eval | unknown | Get aggregated evaluation metrics (precision, recall, etc.). | alternatives |
| query_traces akkireddy-challa/phoenix-mcp-eval | unknown | Run structured queries over trace data. | alternatives |
| get_best_design_model io.github.K1ta141k/mcp-bench-router | unknown | Get the current #1 design model, optionally by category | alternatives |
| query_design_model io.github.K1ta141k/mcp-bench-router | unknown | Send a prompt to the best available model via OpenRouter | alternatives |
| query_specific_model io.github.K1ta141k/mcp-bench-router | unknown | Send a prompt to a specific model via OpenRouter | alternatives |
| reindex_memories uace-mcp | unknown | Backfill embeddings for memories saved before semantic search was enabled. | alternatives |
| glm_recommend glm-mcp-copilot | unknown | Free advisory: GLM vs the default model. | alternatives |
| glm_status glm-mcp-copilot | unknown | Usage ledger (proof of GLM tokens spent) + config. | alternatives |
| update_agent_config pagespace-mcp | unknown | update an agent's system prompt, enabled tools, provider, model, agent definition, tool exposure mode, and visibility (use after create_page | alternatives |
| list_agents pagespace-mcp | unknown | list AI agents in a specific drive | alternatives |
| multi_drive_list_agents pagespace-mcp | unknown | list AI agents across all accessible drives | alternatives |
| foldkit_probe_from_kappa sjgant80-hub/fallaccount-trades-mcp | unknown | κ or text → band + routing op + probe question | alternatives |
| foldkit_signal_survival sjgant80-hub/fallaccount-trades-mcp | unknown | κ^depth signal survival + % | alternatives |
| foldkit_unclog_gain sjgant80-hub/fallaccount-trades-mcp | unknown | gain from clearing N layers of a κ stack | alternatives |
| foldkit_kawasaki_check sjgant80-hub/fallaccount-trades-mcp | unknown | flat-fold angle sum check | alternatives |
| foldkit_maekawa_check sjgant80-hub/fallaccount-trades-mcp | unknown | mountain-valley parity check ( |M−V| = 2 ) | alternatives |
| rank_candidates io.github.Aitejiu/jev | unknown | 候选片段按相关性打分排序（RAG 精排） | alternatives |
| extract_text_from_image io.github.Akhiyugo/kanto-labs-mcp | unknown | OCR text from images, screenshots, photos and scanned PDFs, in reading order with confidence and optional line boxes. | alternatives |
| deepseek_generate deepzhun/DeepSeek MCP Server | unknown | Single-turn text with DeepSeek-V3 (system instruction, temperature, JSON mode) | alternatives |
| deepseek_chat deepzhun/DeepSeek MCP Server | unknown | Multi-turn conversation; switch to deepseek-reasoner for step-by-step thinking | alternatives |
| deepseek_reason deepzhun/DeepSeek MCP Server | unknown | Hard problems with DeepSeek-R1 — returns the reasoning trace and the answer | alternatives |
| deepseek_list_models deepzhun/DeepSeek MCP Server | unknown | List models available to your key | alternatives |
| gemini_generate deepzhun/Gemini MCP Server | unknown | Single-turn text generation (system instruction, temperature, JSON mode) | alternatives |
| gemini_chat deepzhun/Gemini MCP Server | unknown | Multi-turn conversation with full history | alternatives |
| gemini_vision deepzhun/Gemini MCP Server | unknown | Analyze / OCR / describe an image (base64 + prompt) | alternatives |
| gemini_embed deepzhun/Gemini MCP Server | unknown | Text embeddings for search, clustering, RAG | alternatives |
| gemini_count_tokens deepzhun/Gemini MCP Server | unknown | Count tokens for cost & context-window planning | alternatives |
| gemini_list_models deepzhun/Gemini MCP Server | unknown | Discover available models and their limits | alternatives |
| freemodel_run freemodel-mcp | unknown | Execute on a specific model (platform + model name) | alternatives |
| freemodel_key_health freemodel-mcp | unknown | Subscription status, platform health, recommended model | alternatives |
| freemodel_status freemodel-mcp | unknown | Session summary: active model, healthy count | alternatives |
| freemodel_models freemodel-mcp | unknown | List your available platforms and models | alternatives |
| freemodel_recommend freemodel-mcp | unknown | Describe a task → get 2-3 model picks with reasons | alternatives |
| select_ab_test_winner io.github.mrgulshanyadav/misarmail-mcp | unknown | Select the winning variant and send it to the remaining audience. | alternatives |
| generate_subject_lines io.github.mrgulshanyadav/misarmail-mcp | unknown | AI-generate subject line suggestions for a campaign | alternatives |
| categorize_inbox_emails io.github.mrgulshanyadav/misarmail-mcp | unknown | Run AI categorisation over a | alternatives |
| lmstudio_list_models developersorli/mcp-file-system-lmstudio | unknown | List all models currently available on the LM Studio server | alternatives |
| lmstudio_chat developersorli/mcp-file-system-lmstudio | unknown | Send a prompt (or full multi-turn message history) to the local LM Studio model and get its response back | alternatives |
| cs_agent_events codestable/cs-agent-mcp | unknown | Incrementally reads structured events with optional waiting. | alternatives |
| cs_agent_run_structured codestable/cs-agent-mcp | unknown | Atomically runs a one-shot agent and returns strict JSON validated against a schema. | alternatives |
| classify_system_impact CSOAI-ORG/Korea AI Basic Act MCP | unknown | Classifies whether an AI system is 'high-impact' under the Korea AI Basic Act based on its description. | alternatives |
| run_agent Microbiosis/Mini Agent MCP | unknown | Run a sub-agent to complete a task using internal tools and reasoning. | alternatives |
| list_background_models Furkiozknn/mini-creative-toolkit | unknown | rembg models with size, speed, and licence status | alternatives |
| remove_background Furkiozknn/mini-creative-toolkit | unknown | Subject cut-out to a transparent PNG | alternatives |
| upscale_image_fast Furkiozknn/mini-creative-toolkit | unknown | FSRCNN — a real super-resolution CNN, CPU, sub-second | alternatives |
| upscale_image Furkiozknn/mini-creative-toolkit | unknown | Real-ESRGAN via Upscayl — best quality, needs a discrete GPU | alternatives |
| upscale_image_auto Furkiozknn/mini-creative-toolkit | unknown | Picks between them and explains which and why | alternatives |
| humanizer_get_sentiment_tone nicojan/mcp-humanizer | unknown | Neutral bias, emotional layering, subjectivity | alternatives |
| humanizer_get_discourse_cohesion nicojan/mcp-humanizer | unknown | Transitions, markers, cohesive devices, repetition | alternatives |
| humanizer_get_psycholinguistic_texture nicojan/mcp-humanizer | unknown | Cognitive load, self-monitoring, retrieval | alternatives |
| humanizer_get_content_profile nicojan/mcp-humanizer | unknown | Per-content-type adjustments | alternatives |
| humanizer_get_anti_patterns nicojan/mcp-humanizer | unknown | Before/after AI to human rewrites | alternatives |
| humanizer_check_text nicojan/mcp-humanizer | unknown | Deterministic compliance check on supplied text | alternatives |
| analyze_data pypi-ahmad/Local Intelligence Server | unknown | Analyzes arbitrary text using a local Ollama model based on a natural-language instruction. | alternatives |
| predict_attack_path threadlinqs-cmd/Intel Threadlinqs MCP | unknown | Returns ranked next-technique predictions with probability and observation count, plus example threats showing the chain. Built from 4,271 o | alternatives |
| predict_mitre_transitions threadlinqs-cmd/Intel Threadlinqs MCP | unknown | Ranked next-technique predictions with probability and observation count, plus example threats showing the chain. Built from 4,271 observed | alternatives |
| get_similar_threats threadlinqs-cmd/Intel Threadlinqs MCP | unknown | Precomputed-similarity matches by shared TTPs, IOC overlap, and same-actor attribution. | alternatives |
| system_one io.github.psyb0t/decidealot | unknown | Runs the selected model against state and returns typed answers with probabilities | alternatives |
| openai_list_models VouchlyAI/Pincer-MCP | unknown | List all available OpenAI models. | alternatives |
| openai_compatible_chat VouchlyAI/Pincer-MCP | unknown | Chat completions with any OpenAI-compatible API (Azure OpenAI, Ollama, vLLM, etc.). | alternatives |
| openai_compatible_list_models VouchlyAI/Pincer-MCP | unknown | List models from custom OpenAI-compatible endpoints. | alternatives |
| claude_chat VouchlyAI/Pincer-MCP | unknown | Chat completions with Anthropic Claude models (Claude 3.5 Sonnet, Opus, Haiku). | alternatives |
| openrouter_chat VouchlyAI/Pincer-MCP | unknown | Unified API access to 100+ models from multiple providers (OpenAI, Anthropic, Google, Meta, etc.). | alternatives |
| openrouter_list_models VouchlyAI/Pincer-MCP | unknown | List all available models across OpenRouter providers. | alternatives |
| openwebui_chat VouchlyAI/Pincer-MCP | unknown | OpenAI-compatible interface for self-hosted LLMs. | alternatives |
| openwebui_list_models VouchlyAI/Pincer-MCP | unknown | Discover available models on an OpenWebUI instance. | alternatives |
| gemini_generate VouchlyAI/Pincer-MCP | unknown | Secure Google Gemini API calls. | alternatives |
| openai_chat VouchlyAI/Pincer-MCP | unknown | Chat completions with OpenAI GPT models (gpt-4o, gpt-4-turbo, gpt-3.5-turbo, etc.). | alternatives |
| count-tokens com.utilsforagents/utilsforagents-mcp | unknown | Count tokens for LLM input using exact BPE or estimation | alternatives |
| start_optimization wkzMagician/ca-scene-mcp | unknown | Starts an optimization run using the specified plan. | alternatives |
| get_optimization_job wkzMagician/ca-scene-mcp | unknown | Gets the status and details of an optimization job. | alternatives |
| list_optimization_trials wkzMagician/ca-scene-mcp | unknown | Lists all trials for a given optimization job. | alternatives |
| get_optimization_trial wkzMagician/ca-scene-mcp | unknown | Gets details of a specific optimization trial. | alternatives |
As of the latest check, Glimind tracks 200 ml AI agent (MCP) tools, of which 0 are healthy right now. The table below ranks them live-first by real-time reliability. Glimind rates each neutrally (0–100) from safe liveness probes plus privacy-clean real-usage outcomes — see each tool's live page for its current score and a working alternative if it's down.
Ranked live-first by current verdict (healthy → degraded → down), then by the neutral reliability score. Glimind sells no tools, so the ranking is unconflicted.
Every tool here links to its live alternatives — capability-matched substitutes that are healthy now. Or query https://glimind.com/v1/alternatives/{toolId}.
Live data via MCP/REST. Neutral ratings — Glimind only measures.